Forest biomass carbon stock estimates via a novel approach: K-nearest neighbor-based weighted least squares multiple birth support vector regression coupled with whale optimization algorithm

Multiple birth support vector regression (MBSVR) provides fast computation and superior performance but overlooks local sample information and has challenges in parameter selection. The traditional least squares models boast fast computational speed but lack robustness and may struggle with noise and outliers. The carbon storage estimates are easily affected by noise and interference points. MBSVR and least squares models are only partially effective in carbon storage estimates. Consequently, we propose least squares multiple birth support vector regression (LSMBSVR) and K-nearest neighbor-based (KNN) weighted least squares multiple birth support vector regression (WLSMBSVR), which have the following merits. Firstly, both models inherit the strengths of MBSVR. Secondly, they exhibit enhanced fitting accuracy, robust stability, and remarkable anti-interference capability. Thirdly, LSMBSVR offers a faster training speed and maintains a comparable regression performance to MBSVR. Fourthly, WLSMBSVR considers the local information, enhancing its anti-interference capability. Lastly, we employ the whale optimization algorithm (WOA) to improve the effectiveness of parameter selection. Experiment results indicate that our models can be more effective on carbon storage, synthetic, and UCI datasets than compared models, verifying the broad application value of our models.